🌟 Qwen3.5-9B-DeepSeek-V4-Flash

💡 Model Overview & Design

[!NOTE] Qwen3.5-9B-DeepSeek-V4-Flash is an efficient reasoning model distilled using high-quality data from DeepSeek-V4.

• By leveraging the dataset Jackrong/DeepSeek-V4-Distill-8000x, this model successfully transfers the advanced structured reasoning and multi-step problem-solving capabilities of the DeepSeek-V4 architecture into the highly efficient Qwen3.5-9B parameter space.
• This model was trained in an Unsloth environment, prioritizing stable gradient propagation and rigorous data curation to ensure the distillation process avoids merely learning "hollow chain-of-thought" and instead captures genuine logical generalization.

• 🧩 Structured Reasoning: Inheriting DeepSeek-V4's deep logic capabilities.
• ⚡ Flash Inference: Maintaining the token-efficiency and speed of the 9B parameter size.
• 🔧 Tool-augmented Workflows: Reliable agentic action generation.

🍎 About the Teacher Model: DeepSeek-V4

• 🏆 World-Class Reasoning & Coding: DeepSeek-V4 demonstrates elite performance in mathematics (MATH-500), STEM subjects, and real-world software engineering (SWE-bench). Its "Think" modes provide the sophisticated Long-CoT (Chain-of-Thought) traces that define this model's logic.
• 🧠 Architectural Innovation: * Hybrid Attention & DSA: Features Token-level compression and DeepSeek Sparse Attention, which reduces KV Cache memory overhead by up to 90%, allowing for highly efficient long-context processing.
  • Engram Memory & mHC: Utilizes Manifold-constrained Hyper-connections to decouple factual knowledge retrieval from dynamic logical reasoning, ensuring exceptional stability and generalization.
• 🤖 Agent-Centric Design: Specifically optimized for multi-step tool calling and complex environment interaction, ensuring that the distilled knowledge includes reliable "how-to-act" procedures, not just "how-to-talk."

🤝 Collaboration & Training Details

• 🖥️ Hardware: NVIDIA DGX
• 💾 Training Data: DeepSeek-V4-Distill-8000x
• 🧪 Training Method: Distillation

🎯 Motivation & Distillation Insights

• 🧠 Latent Knowledge Activation: DeepSeek-V4's reasoning traces help the Qwen3.5-9B model activate its existing latent knowledge more effectively.
• 🏗️ Learning Procedures: The model learns actual problem-solving procedures, not just the output format.
• 🚀 Efficiency: The 8000x dataset provides a dense signal, allowing the 9B model to converge on reasoning tasks much faster than traditional large-scale SFT.

📊 Evaluation

[!IMPORTANT] This is an early controlled Q5_K_M comparison between Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash and the official Qwen3.5-9B base model.

This evaluation was completed by Kyle Hessling, who ran the same evaluation suite twice under the same local inference conditions: once on the DeepSeek-V4 distill model and once on the official Qwen3.5-9B base model.

• ❤️ Special thanks to Kyle for the careful post-training testing and detailed comparison report. You can find him on X/Twitter here: @KyleHessling1.
• 📄 Full evaluation report: KyleHessling1/jackrong-deepseek-9b-eval.

🔬 Supporting Evidence

• High-quality long-CoT data from DeepSeek-V4 enables cross-domain transfer.
• Optimization Discipline: Short, highly-curated distillation (8000 examples) prevents the model from overfitting to the teacher's stylistic quirks while preserving the core reasoning engine.

🛠️ Best Practices

• temperature=0.7 to 1.0 (Use lower temperature for strict coding tasks, higher for creative reasoning)
• top_p=0.95

📚 Resources & Guides

📥 Core Technical Document

A Note: My goal isn't just to detail a workflow, but to demystify LLM training. Beyond the social media hype, fine-tuning isn't an unattainable ritual—often, all you need is a Google account, a standard laptop, and relentless curiosity. All training and testing for this project were self-funded. If you find this model or guide helpful, a Star ⭐️ on GitHub would be the greatest encouragement. Thank you! 🙏

⚠️ Limitations

• Parameter Constraints: While enhanced by DeepSeek-V4 distillation, the model is still bound by the 9B parameter limits and may struggle with extremely obscure knowledge.
• Over-reasoning: On very simple queries, the model might still attempt to produce a lengthy reasoning chain due to the SFT bias.
• Safety Trade-offs: Asymmetric gains mean that while reasoning improves, certain alignment-sensitive behaviors might regress.

🙏 Acknowledgements

• DeepSeek Team for the foundational advancements in the V4 architecture.
• Unsloth for efficient fine-tuning frameworks.
• Open-source datasets and community contributors.
• Researchers exploring reasoning SFT and distillation.

📖 Citation